SPARK: Harnessing Human-Centered Workflows with Biomedical Foundation Models for Drug Discovery
Abstract
Early detection of Alzheimer's disease (AD) through spontaneous speech analysis represents a promising, non-invasive diagnostic approach. Existing methods predominantly rely on fusion-based multimodal deep learning, effectively integrating linguistic and acoustic features. However, these methods inadequately model higher-order interactions between modalities, reducing diagnostic accuracy. To address this, we introduce SpeechHGT, a multimodal hypergraph transformer designed to capture and learn higher-order interactions in spontaneous speech features. SpeechHGT encodes multimodal features as hypergraphs, where nodes represent individual features and hyperedges represent grouped interactions. A novel hypergraph attention mechanism enables robust modeling of both pairwise and higher-order interactions. Experimental evaluations on the DementiaBank datasets reveal that SpeechHGT achieves state-of-the-art performance, surpassing baseline models in accuracy and F1 score. These results highlight the potential of hypergraph-based models to improve AI-driven diagnostic tools for early AD detection.
Cite
Text
Kwon et al. "SPARK: Harnessing Human-Centered Workflows with Biomedical Foundation Models for Drug Discovery." International Joint Conference on Artificial Intelligence, 2024. doi:10.24963/ijcai.2024/1015Markdown
[Kwon et al. "SPARK: Harnessing Human-Centered Workflows with Biomedical Foundation Models for Drug Discovery." International Joint Conference on Artificial Intelligence, 2024.](https://mlanthology.org/ijcai/2024/kwon2024ijcai-spark/) doi:10.24963/ijcai.2024/1015BibTeX
@inproceedings{kwon2024ijcai-spark,
title = {{SPARK: Harnessing Human-Centered Workflows with Biomedical Foundation Models for Drug Discovery}},
author = {Kwon, Bum Chul and Rabinovici-Cohen, Simona and Moturi, Beldine and Mwaura, Ruth and Wahome, Kezia and Njeru, Oliver and Shinyenyi, Miguel and Wanjiru, Catherine and Remy, Sekou L. and Ogallo, William and Guez, Itai and Suryanarayanan, Parthasarathy and Morrone, Joseph A. and Sethi, Shreyans and Kang, Seung-gu and Huynh, Tien and Ng, Kenney and Mahajan, Diwakar and Li, Hongyang and Ninio, Matan and Ayati, Shervin and Hexter, Efrat and Cornell, Wendy D.},
booktitle = {International Joint Conference on Artificial Intelligence},
year = {2024},
pages = {8713-8716},
doi = {10.24963/ijcai.2024/1015},
url = {https://mlanthology.org/ijcai/2024/kwon2024ijcai-spark/}
}